Overview

Dataset statistics

Number of variables19
Number of observations47511
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory148.0 B

Variable types

Numeric17
Categorical2

Warnings

df_index is highly correlated with track_hash and 2 other fieldsHigh correlation
track_hash is highly correlated with df_index and 1 other fieldsHigh correlation
popularity is highly correlated with df_index and 1 other fieldsHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
instrumentalness is highly correlated with loudnessHigh correlation
loudness is highly correlated with acousticness and 2 other fieldsHigh correlation
music_genre is highly correlated with df_index and 2 other fieldsHigh correlation
df_index is highly correlated with track_hash and 2 other fieldsHigh correlation
track_hash is highly correlated with df_index and 1 other fieldsHigh correlation
popularity is highly correlated with df_index and 1 other fieldsHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with acousticness and 1 other fieldsHigh correlation
music_genre is highly correlated with df_index and 2 other fieldsHigh correlation
df_index is highly correlated with music_genreHigh correlation
track_hash is highly correlated with music_genreHigh correlation
acousticness is highly correlated with energyHigh correlation
energy is highly correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly correlated with energyHigh correlation
music_genre is highly correlated with df_index and 1 other fieldsHigh correlation
track_hash is highly correlated with music_genre and 3 other fieldsHigh correlation
speechiness is highly correlated with music_genre and 1 other fieldsHigh correlation
loudness is highly correlated with instrumentalness and 5 other fieldsHigh correlation
instrumentalness is highly correlated with loudness and 2 other fieldsHigh correlation
valence is highly correlated with danceability and 1 other fieldsHigh correlation
acousticness is highly correlated with loudness and 3 other fieldsHigh correlation
music_genre is highly correlated with track_hash and 8 other fieldsHigh correlation
popularity is highly correlated with track_hash and 2 other fieldsHigh correlation
df_index is highly correlated with track_hash and 8 other fieldsHigh correlation
danceability is highly correlated with loudness and 4 other fieldsHigh correlation
energy is highly correlated with track_hash and 6 other fieldsHigh correlation
df_index is uniformly distributed Uniform
track_hash is uniformly distributed Uniform
df_index has unique values Unique
music_genre has 4728 (10.0%) zeros Zeros

Reproduction

Analysis started2021-09-09 16:10:02.844313
Analysis finished2021-09-09 16:11:21.229864
Duration1 minute and 18.39 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct47511
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25024.83012
Minimum0
Maximum49999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size371.3 KiB
2021-09-10T00:11:21.355862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2510.5
Q112559.5
median25044
Q337498.5
95-th percentile47496.5
Maximum49999
Range49999
Interquartile range (IQR)24939

Descriptive statistics

Standard deviation14419.58228
Coefficient of variation (CV)0.5762109956
Kurtosis-1.19704521
Mean25024.83012
Median Absolute Deviation (MAD)12469
Skewness-0.00271137893
Sum1188954704
Variance207924353.1
MonotonicityStrictly increasing
2021-09-10T00:11:21.565876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
498691
 
< 0.1%
211831
 
< 0.1%
437121
 
< 0.1%
416651
 
< 0.1%
478101
 
< 0.1%
457631
 
< 0.1%
355241
 
< 0.1%
334771
 
< 0.1%
396221
 
< 0.1%
Other values (47501)47501
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
499991
< 0.1%
499981
< 0.1%
499971
< 0.1%
499961
< 0.1%
499951
< 0.1%
499941
< 0.1%
499931
< 0.1%
499921
< 0.1%
499911
< 0.1%
499901
< 0.1%

artist_name
Real number (ℝ)

Distinct6862
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.076783839 × 10-16
Minimum-1.721086277
Maximum1.729506856
Zeros0
Zeros (%)0.0%
Negative24057
Negative (%)50.6%
Memory size371.3 KiB
2021-09-10T00:11:21.736859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.721086277
5-th percentile-1.552605196
Q1-0.8253704382
median-0.0307432481
Q30.8423408038
95-th percentile1.588686848
Maximum1.729506856
Range3.450593134
Interquartile range (IQR)1.667711242

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-9.287012749 × 1015
Kurtosis-1.169007965
Mean-1.076783839 × 10-16
Median Absolute Deviation (MAD)0.838884907
Skewness0.0359342833
Sum-5.115907697 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:21.900863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4430154943429
 
0.9%
1.588686848402
 
0.8%
0.1146031177317
 
0.7%
-0.2510259755314
 
0.7%
-0.6644332858241
 
0.5%
-0.8746574411172
 
0.4%
-1.217151819169
 
0.4%
1.637973851167
 
0.4%
-0.8022357225147
 
0.3%
-0.6583981425124
 
0.3%
Other values (6852)45029
94.8%
ValueCountFrequency (%)
-1.72108627722
 
< 0.1%
-1.7205833491
 
< 0.1%
-1.720080423
 
< 0.1%
-1.7195774911
 
< 0.1%
-1.71907456392
0.2%
-1.7185716342
 
< 0.1%
-1.7180687061
 
< 0.1%
-1.7175657775
 
< 0.1%
-1.7170628487
 
< 0.1%
-1.716559922
 
< 0.1%
ValueCountFrequency (%)
1.72950685610
< 0.1%
1.7290039281
 
< 0.1%
1.7285009991
 
< 0.1%
1.7279980712
 
< 0.1%
1.7274951421
 
< 0.1%
1.7269922131
 
< 0.1%
1.7264892851
 
< 0.1%
1.7259863564
 
< 0.1%
1.7254834288
< 0.1%
1.7249804994
 
< 0.1%

track_hash
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct41248
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.148569428 × 10-16
Minimum-1.729783538
Maximum1.730940765
Zeros0
Zeros (%)0.0%
Negative23776
Negative (%)50.0%
Memory size371.3 KiB
2021-09-10T00:11:22.158862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.729783538
5-th percentile-1.558161079
Q1-0.8675600235
median-0.001309191481
Q30.8651094454
95-th percentile1.558563184
Maximum1.730940765
Range3.460724303
Interquartile range (IQR)1.732669469

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)8.706574452 × 1015
Kurtosis-1.201360223
Mean1.148569428 × 10-16
Median Absolute Deviation (MAD)0.8663766857
Skewness-0.0007803480472
Sum5.456968211 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:22.320860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.93042750176
 
< 0.1%
-0.71473171316
 
< 0.1%
1.6548412445
 
< 0.1%
-0.58141072215
 
< 0.1%
0.87068895825
 
< 0.1%
0.95853482215
 
< 0.1%
0.61285673335
 
< 0.1%
0.39387134215
 
< 0.1%
0.87278651955
 
< 0.1%
1.1322128915
 
< 0.1%
Other values (41238)47459
99.9%
ValueCountFrequency (%)
-1.7297835381
< 0.1%
-1.7296996351
< 0.1%
-1.7296157331
< 0.1%
-1.7295318311
< 0.1%
-1.7294479281
< 0.1%
-1.7293640261
< 0.1%
-1.7292801231
< 0.1%
-1.7291962211
< 0.1%
-1.7291123181
< 0.1%
-1.7290284161
< 0.1%
ValueCountFrequency (%)
1.7309407651
< 0.1%
1.7308568632
< 0.1%
1.730772961
< 0.1%
1.7306890581
< 0.1%
1.7306051551
< 0.1%
1.7305212531
< 0.1%
1.730437352
< 0.1%
1.7303534481
< 0.1%
1.7302695461
< 0.1%
1.7301856431
< 0.1%

track_name
Real number (ℝ)

Distinct39917
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.459640315 × 10-16
Minimum-1.731675933
Maximum1.757221333
Zeros0
Zeros (%)0.0%
Negative23840
Negative (%)50.2%
Memory size371.3 KiB
2021-09-10T00:11:22.488858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.731675933
5-th percentile-1.551925526
Q1-0.8615056571
median-0.006106992519
Q30.8603922321
95-th percentile1.564884465
Maximum1.757221333
Range3.488897266
Interquartile range (IQR)1.721897889

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)6.851074979 × 1015
Kurtosis-1.193975662
Mean1.459640315 × 10-16
Median Absolute Deviation (MAD)0.8611237566
Skewness0.01351652697
Sum6.934897101 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:22.642862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.509740273616
 
< 0.1%
-0.760770260314
 
< 0.1%
1.41441506313
 
< 0.1%
-0.798529645512
 
< 0.1%
1.54744697112
 
< 0.1%
-0.953063425612
 
< 0.1%
-0.921684677312
 
< 0.1%
0.963837214411
 
< 0.1%
0.800475429911
 
< 0.1%
-0.453101195810
 
< 0.1%
Other values (39907)47388
99.7%
ValueCountFrequency (%)
-1.7316759331
< 0.1%
-1.7315885271
< 0.1%
-1.7315011211
< 0.1%
-1.7314137151
< 0.1%
-1.7313263091
< 0.1%
-1.7312389031
< 0.1%
-1.7311514971
< 0.1%
-1.7310640911
< 0.1%
-1.7309766851
< 0.1%
-1.7308892791
< 0.1%
ValueCountFrequency (%)
1.7572213331
< 0.1%
1.7571339271
< 0.1%
1.7570465211
< 0.1%
1.7569591151
< 0.1%
1.7568717091
< 0.1%
1.7567843031
< 0.1%
1.7566968971
< 0.1%
1.7566094911
< 0.1%
1.7565220851
< 0.1%
1.7564346791
< 0.1%

popularity
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct99
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.105710618 × 10-16
Minimum-2.844925579
Maximum3.525124643
Zeros0
Zeros (%)0.0%
Negative23373
Negative (%)49.2%
Memory size371.3 KiB
2021-09-10T00:11:22.822863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.844925579
5-th percentile-1.68673463
Q1-0.6572315635
median0.05055179453
Q30.7583351526
95-th percentile1.530462452
Maximum3.525124643
Range6.370050223
Interquartile range (IQR)1.415566716

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)4.74904061 × 1015
Kurtosis0.01552711195
Mean2.105710618 × 10-16
Median Absolute Deviation (MAD)0.7077833581
Skewness-0.3064619664
Sum1.000444172 × 10-11
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:22.974863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5009593861247
 
2.6%
0.62964726931231
 
2.6%
0.56530332771213
 
2.6%
0.37227150271202
 
2.5%
0.6939912111194
 
2.5%
0.75833515261171
 
2.5%
0.43661544441166
 
2.5%
-0.3998557971156
 
2.4%
-0.52854368031150
 
2.4%
-0.46419973861134
 
2.4%
Other values (89)35647
75.0%
ValueCountFrequency (%)
-2.844925579658
1.4%
-2.78058163830
 
0.1%
-2.71623769650
 
0.1%
-2.65189375440
 
0.1%
-2.58754981341
 
0.1%
-2.52320587127
 
0.1%
-2.4588619313
 
< 0.1%
-2.39451798821
 
< 0.1%
-2.33017404643
 
0.1%
-2.26583010556
 
0.1%
ValueCountFrequency (%)
3.5251246431
 
< 0.1%
3.396436761
 
< 0.1%
3.3320928182
 
< 0.1%
3.2677488772
 
< 0.1%
3.2034049351
 
< 0.1%
3.1390609932
 
< 0.1%
3.0747170521
 
< 0.1%
3.010373112
 
< 0.1%
2.9460291686
< 0.1%
2.8816852277
< 0.1%

acousticness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4162
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.571411902 × 10-18
Minimum-0.8971117969
Maximum2.02371963
Zeros0
Zeros (%)0.0%
Negative30054
Negative (%)63.3%
Memory size371.3 KiB
2021-09-10T00:11:23.145863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.8971117969
5-th percentile-0.8959593001
Q1-0.8384605634
median-0.4748229159
Q30.7187296852
95-th percentile1.97093352
Maximum2.02371963
Range2.920831427
Interquartile range (IQR)1.557190249

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-1.044788934 × 1017
Kurtosis-0.7156543301
Mean-9.571411902 × 10-18
Median Absolute Deviation (MAD)0.4138431034
Skewness0.884777621
Sum-4.547473509 × 10-13
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:23.306862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.020787068260
 
0.5%
2.017854507229
 
0.5%
2.011989383208
 
0.4%
2.014921945189
 
0.4%
2.009056821151
 
0.3%
2.00612426144
 
0.3%
2.003191698126
 
0.3%
1.991461451112
 
0.2%
1.982663766110
 
0.2%
1.997326575107
 
0.2%
Other values (4152)45875
96.6%
ValueCountFrequency (%)
-0.89711179691
 
< 0.1%
-0.89710880571
 
< 0.1%
-0.89710807251
 
< 0.1%
-0.89710777931
 
< 0.1%
-0.89710774991
 
< 0.1%
-0.89710772063
< 0.1%
-0.89710751531
 
< 0.1%
-0.89710725141
 
< 0.1%
-0.89710719281
 
< 0.1%
-0.89710710481
 
< 0.1%
ValueCountFrequency (%)
2.0237196379
 
0.2%
2.020787068260
0.5%
2.017854507229
0.5%
2.014921945189
0.4%
2.011989383208
0.4%
2.009056821151
0.3%
2.00612426144
0.3%
2.003191698126
0.3%
2.00025913698
 
0.2%
1.997326575107
0.2%

danceability
Real number (ℝ)

HIGH CORRELATION

Distinct1083
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.785705951 × 10-17
Minimum-2.791973762
Maximum2.393419475
Zeros0
Zeros (%)0.0%
Negative22735
Negative (%)47.9%
Memory size371.3 KiB
2021-09-10T00:11:23.475863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.791973762
5-th percentile-1.821391738
Q1-0.6515437385
median0.05372347638
Q30.7198091793
95-th percentile1.559413007
Maximum2.393419475
Range5.185393237
Interquartile range (IQR)1.371352918

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-2.089577868 × 1016
Kurtosis-0.2989001137
Mean-4.785705951 × 10-17
Median Absolute Deviation (MAD)0.6828777795
Skewness-0.3014020766
Sum-2.273736754 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:23.652864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1645735187133
 
0.3%
0.5518884139133
 
0.3%
-0.3324942841129
 
0.3%
-0.06382105943128
 
0.3%
0.288812548127
 
0.3%
0.1712680122126
 
0.3%
0.3503834953122
 
0.3%
0.3895650073121
 
0.3%
-0.0246395475121
 
0.3%
-0.2989101311120
 
0.3%
Other values (1073)46251
97.3%
ValueCountFrequency (%)
-2.7919737621
< 0.1%
-2.7897348181
< 0.1%
-2.7886153471
< 0.1%
-2.7863764032
< 0.1%
-2.7858166672
< 0.1%
-2.784137461
< 0.1%
-2.7824582521
< 0.1%
-2.7818985161
< 0.1%
-2.7807790441
< 0.1%
-2.7802193081
< 0.1%
ValueCountFrequency (%)
2.3934194751
 
< 0.1%
2.3598353222
< 0.1%
2.3542379631
 
< 0.1%
2.3486406042
< 0.1%
2.3430432452
< 0.1%
2.3374458861
 
< 0.1%
2.3262511691
 
< 0.1%
2.320653811
 
< 0.1%
2.3150564512
< 0.1%
2.3094590924
< 0.1%

duration_ms
Real number (ℝ)

Distinct25167
Distinct (%)53.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-2.823566511 × 10-16
Minimum-2.181305949
Maximum43.5020136
Zeros0
Zeros (%)0.0%
Negative30852
Negative (%)64.9%
Memory size371.3 KiB
2021-09-10T00:11:23.825859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.181305949
5-th percentile-1.061328166
Q1-0.4794776609
median-0.08958336416
Q30.2194820228
95-th percentile1.479334284
Maximum43.5020136
Range45.68331954
Interquartile range (IQR)0.6989596836

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-3.541657404 × 1015
Kurtosis215.2874484
Mean-2.823566511 × 10-16
Median Absolute Deviation (MAD)0.3593207699
Skewness7.934599935
Sum-1.341504685 × 10-11
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:23.988894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.761231158 × 10-164696
 
9.9%
-0.0514435417333
 
0.1%
-0.506844406628
 
0.1%
-0.620694622827
 
0.1%
-0.355044118319
 
< 0.1%
-0.279143974219
 
< 0.1%
-0.563769514718
 
< 0.1%
-0.567564521917
 
< 0.1%
-0.430944262416
 
< 0.1%
-0.260168938116
 
< 0.1%
Other values (25157)42622
89.7%
ValueCountFrequency (%)
-2.1813059491
< 0.1%
-2.1736495221
< 0.1%
-2.1416101741
< 0.1%
-2.1377867041
< 0.1%
-2.1126068311
< 0.1%
-2.1007474341
< 0.1%
-2.0789925551
< 0.1%
-2.0749698471
< 0.1%
-2.0538126821
< 0.1%
-2.0529303431
< 0.1%
ValueCountFrequency (%)
43.50201361
< 0.1%
40.346351251
< 0.1%
38.240179181
< 0.1%
27.988346711
< 0.1%
19.435918471
< 0.1%
16.829630851
< 0.1%
16.037366181
< 0.1%
15.438381211
< 0.1%
14.544533681
< 0.1%
13.016028111
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2065
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.627140023 × 10-16
Minimum-2.268510014
Maximum1.510167443
Zeros0
Zeros (%)0.0%
Negative21013
Negative (%)44.2%
Memory size371.3 KiB
2021-09-10T00:11:24.152865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.268510014
5-th percentile-1.994033808
Q1-0.6286180238
median0.1625433258
Q30.8174280793
95-th percentile1.336036237
Maximum1.510167443
Range3.778677457
Interquartile range (IQR)1.446046103

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-6.14581726 × 1015
Kurtosis-0.587293987
Mean-1.627140023 × 10-16
Median Absolute Deviation (MAD)0.7078812075
Skewness-0.5716370046
Sum-7.730704965 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:24.327894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.454023823196
 
0.2%
0.870424533495
 
0.2%
0.283678077994
 
0.2%
0.980202902594
 
0.2%
0.775788008393
 
0.2%
0.847711767489
 
0.2%
0.43131105788
 
0.2%
1.14297772688
 
0.2%
0.968846519587
 
0.2%
0.938562831486
 
0.2%
Other values (2055)46601
98.1%
ValueCountFrequency (%)
-2.2685100141
< 0.1%
-2.2683850941
< 0.1%
-2.2681011841
< 0.1%
-2.2679384091
< 0.1%
-2.2679005551
< 0.1%
-2.2676847831
< 0.1%
-2.267571221
< 0.1%
-2.267495512
< 0.1%
-2.2674198011
< 0.1%
-2.2673440921
< 0.1%
ValueCountFrequency (%)
1.5101674435
 
< 0.1%
1.50638198216
 
< 0.1%
1.50259652117
 
< 0.1%
1.4988110629
0.1%
1.49502559943
0.1%
1.49124013835
0.1%
1.48745467728
0.1%
1.48366921636
0.1%
1.47988375549
0.1%
1.47609829442
0.1%

instrumentalness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION

Distinct5101
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.571411902 × 10-18
Minimum-0.5585759203
Maximum2.501600129
Zeros0
Zeros (%)0.0%
Negative35912
Negative (%)75.6%
Memory size371.3 KiB
2021-09-10T00:11:24.512859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.5585759203
5-th percentile-0.5585759203
Q1-0.5585759203
median-0.5580873982
Q3-0.08080747074
95-th percentile2.231223129
Maximum2.501600129
Range3.06017605
Interquartile range (IQR)0.4777684495

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)1.044788934 × 1017
Kurtosis0.4513913283
Mean9.571411902 × 10-18
Median Absolute Deviation (MAD)0.0004885220802
Skewness1.485120446
Sum4.547473509 × 10-13
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:24.675889image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.558575920314248
30.0%
2.2004984767
 
0.1%
2.21278833466
 
0.1%
2.19742600462
 
0.1%
2.24351299362
 
0.1%
2.17899120961
 
0.1%
2.20664340259
 
0.1%
2.27731011859
 
0.1%
2.31110724358
 
0.1%
2.25887532257
 
0.1%
Other values (5091)32712
68.9%
ValueCountFrequency (%)
-0.558575920314248
30.0%
-0.55857284783
 
< 0.1%
-0.558572817125
 
0.1%
-0.558572786416
 
< 0.1%
-0.558572755619
 
< 0.1%
-0.558572724918
 
< 0.1%
-0.558572694218
 
< 0.1%
-0.558572663516
 
< 0.1%
-0.558572632718
 
< 0.1%
-0.55857260216
 
< 0.1%
ValueCountFrequency (%)
2.5016001291
 
< 0.1%
2.4954551981
 
< 0.1%
2.4923827322
 
< 0.1%
2.4893102662
 
< 0.1%
2.4800928681
 
< 0.1%
2.4770204023
< 0.1%
2.4739479363
< 0.1%
2.470875473
< 0.1%
2.4678030045
< 0.1%
2.4647305395
< 0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.101775088 × 10-17
Minimum-1.548514114
Maximum1.632191426
Zeros0
Zeros (%)0.0%
Negative26742
Negative (%)56.3%
Memory size371.3 KiB
2021-09-10T00:11:24.822864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.548514114
5-th percentile-1.548514114
Q1-0.6810489666
median-0.1027388685
Q30.7647262786
95-th percentile1.632191426
Maximum1.632191426
Range3.18070554
Interquartile range (IQR)1.445775245

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-1.638884603 × 1016
Kurtosis-1.227188854
Mean-6.101775088 × 10-17
Median Absolute Deviation (MAD)0.8674651471
Skewness0.08268596361
Sum-2.899014362 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:24.935893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1.3430363775429
11.4%
-0.68104896665223
11.0%
-0.39189391765136
10.8%
-0.10273886854993
10.5%
-1.5485141144591
9.7%
0.76472627864139
8.7%
-0.97020401573607
7.6%
0.47557122963568
7.5%
-1.2593590653192
6.7%
1.6321914263164
6.7%
Other values (2)4469
9.4%
ValueCountFrequency (%)
-1.5485141144591
9.7%
-1.2593590653192
6.7%
-0.97020401573607
7.6%
-0.68104896665223
11.0%
-0.39189391765136
10.8%
-0.10273886854993
10.5%
0.18641618051517
 
3.2%
0.47557122963568
7.5%
0.76472627864139
8.7%
1.0538813282952
6.2%
ValueCountFrequency (%)
1.6321914263164
6.7%
1.3430363775429
11.4%
1.0538813282952
6.2%
0.76472627864139
8.7%
0.47557122963568
7.5%
0.18641618051517
 
3.2%
-0.10273886854993
10.5%
-0.39189391765136
10.8%
-0.68104896665223
11.0%
-0.97020401573607
7.6%

liveness
Real number (ℝ)

Distinct1639
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.024991249 × 10-17
Minimum-1.14012787
Maximum4.988339897
Zeros0
Zeros (%)0.0%
Negative32363
Negative (%)68.1%
Memory size371.3 KiB
2021-09-10T00:11:25.087865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.14012787
5-th percentile-0.8144371826
Q1-0.6003217012
median-0.4202419177
Q30.3099785104
95-th percentile2.206695131
Maximum4.988339897
Range6.128467767
Interquartile range (IQR)0.9103002116

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-1.99007416 × 1016
Kurtosis5.708006136
Mean-5.024991249 × 10-17
Median Absolute Deviation (MAD)0.2741420759
Skewness2.248357453
Sum-2.387423592 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:25.256860image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5192548571598
 
1.3%
-0.5316314745580
 
1.2%
-0.5130665484579
 
1.2%
-0.5378197832520
 
1.1%
-0.5254431658516
 
1.1%
-0.5068782397497
 
1.0%
-0.5563847093485
 
1.0%
-0.5501964006467
 
1.0%
-0.5440080919461
 
1.0%
-0.5625730181459
 
1.0%
Other values (1629)42349
89.1%
ValueCountFrequency (%)
-1.140127871
< 0.1%
-1.1158078171
< 0.1%
-1.1028123691
< 0.1%
-1.0953863981
< 0.1%
-1.0929110751
< 0.1%
-1.0836286122
< 0.1%
-1.0817721191
< 0.1%
-1.0799156261
< 0.1%
-1.0786779651
< 0.1%
-1.0737273181
< 0.1%
ValueCountFrequency (%)
4.9883398972
< 0.1%
4.9635866621
 
< 0.1%
4.9450217361
 
< 0.1%
4.9388334271
 
< 0.1%
4.9326451182
< 0.1%
4.926456813
< 0.1%
4.9202685012
< 0.1%
4.9140801923
< 0.1%
4.9078918832
< 0.1%
4.9017035752
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct16864
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.339997666 × 10-16
Minimum-6.163531779
Maximum2.091670296
Zeros0
Zeros (%)0.0%
Negative16511
Negative (%)34.8%
Memory size371.3 KiB
2021-09-10T00:11:25.478866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-6.163531779
5-th percentile-2.241132407
Q1-0.2798920875
median0.3005238725
Q30.6418494161
95-th percentile0.9848003194
Maximum2.091670296
Range8.255202075
Interquartile range (IQR)0.9217415036

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)7.462778102 × 1015
Kurtosis4.020996997
Mean1.339997666 × 10-16
Median Absolute Deviation (MAD)0.4130039077
Skewness-1.873985086
Sum6.366462912 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:25.661863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.598452311318
 
< 0.1%
0.648838462917
 
< 0.1%
0.510845421716
 
< 0.1%
0.575047131116
 
< 0.1%
0.571958947616
 
< 0.1%
0.334656426916
 
< 0.1%
0.668342779716
 
< 0.1%
0.408610294615
 
< 0.1%
0.425188963914
 
< 0.1%
0.499305367614
 
< 0.1%
Other values (16854)47353
99.7%
ValueCountFrequency (%)
-6.1635317791
< 0.1%
-6.1536170841
< 0.1%
-6.0759248891
< 0.1%
-6.0133485391
< 0.1%
-5.734436811
< 0.1%
-5.686001091
< 0.1%
-5.6759238591
< 0.1%
-5.5720633721
< 0.1%
-5.5278535881
< 0.1%
-5.5099746311
< 0.1%
ValueCountFrequency (%)
2.0916702961
< 0.1%
1.7999182251
< 0.1%
1.790816211
< 0.1%
1.744818531
< 0.1%
1.740755131
< 0.1%
1.7012588891
< 0.1%
1.6967078821
< 0.1%
1.6903689791
< 0.1%
1.6494099131
< 0.1%
1.6476220181
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size371.3 KiB
-0.747331373879606
30485 
1.3380944985739334
17026 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters855198
Distinct characters11
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row-0.747331373879606
2nd row1.3380944985739334
3rd row-0.747331373879606
4th row-0.747331373879606
5th row1.3380944985739334

Common Values

ValueCountFrequency (%)
-0.74733137387960630485
64.2%
1.338094498573933417026
35.8%

Length

2021-09-10T00:11:26.069862image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-10T00:11:26.224861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.74733137387960630485
64.2%
1.338094498573933417026
35.8%

Most occurring characters

ValueCountFrequency (%)
3207070
24.2%
7138966
16.2%
481563
 
9.5%
981563
 
9.5%
077996
 
9.1%
864537
 
7.5%
660970
 
7.1%
.47511
 
5.6%
147511
 
5.6%
-30485
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number777202
90.9%
Other Punctuation47511
 
5.6%
Dash Punctuation30485
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3207070
26.6%
7138966
17.9%
481563
 
10.5%
981563
 
10.5%
077996
 
10.0%
864537
 
8.3%
660970
 
7.8%
147511
 
6.1%
517026
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-30485
100.0%
Other Punctuation
ValueCountFrequency (%)
.47511
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common855198
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3207070
24.2%
7138966
16.2%
481563
 
9.5%
981563
 
9.5%
077996
 
9.1%
864537
 
7.5%
660970
 
7.1%
.47511
 
5.6%
147511
 
5.6%
-30485
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII855198
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3207070
24.2%
7138966
16.2%
481563
 
9.5%
981563
 
9.5%
077996
 
9.1%
864537
 
7.5%
660970
 
7.1%
.47511
 
5.6%
147511
 
5.6%
-30485
 
3.6%

speechiness
Real number (ℝ)

HIGH CORRELATION

Distinct1332
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.244283547 × 10-16
Minimum-0.7043794893
Maximum8.391631955
Zeros0
Zeros (%)0.0%
Negative35106
Negative (%)73.9%
Memory size371.3 KiB
2021-09-10T00:11:26.402863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-0.7043794893
5-th percentile-0.647016364
Q1-0.5678948118
median-0.4413003283
Q30.05024231471
95-th percentile2.25971166
Maximum8.391631955
Range9.096011444
Interquartile range (IQR)0.6181371265

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-8.036837956 × 1015
Kurtosis7.393061602
Mean-1.244283547 × 10-16
Median Absolute Deviation (MAD)0.1691223178
Skewness2.46969695
Sum-5.911715562 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:26.569894image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.5965763745163
 
0.3%
-0.5916312775149
 
0.3%
-0.6133897043144
 
0.3%
-0.565916773143
 
0.3%
-0.5995434327141
 
0.3%
-0.5926202969141
 
0.3%
-0.57086187140
 
0.3%
-0.5936093163140
 
0.3%
-0.5787740252139
 
0.3%
-0.5669057924138
 
0.3%
Other values (1322)46073
97.0%
ValueCountFrequency (%)
-0.70437948931
 
< 0.1%
-0.70339046993
 
< 0.1%
-0.70240145051
 
< 0.1%
-0.70141243112
 
< 0.1%
-0.70042341171
 
< 0.1%
-0.69943439236
< 0.1%
-0.69844537293
 
< 0.1%
-0.69745635352
 
< 0.1%
-0.69646733418
< 0.1%
-0.69547831474
< 0.1%
ValueCountFrequency (%)
8.3916319551
 
< 0.1%
8.3817417611
 
< 0.1%
8.3619613731
 
< 0.1%
8.2927300141
 
< 0.1%
8.2432790441
 
< 0.1%
8.1938280743
< 0.1%
8.1740476861
 
< 0.1%
8.1542672982
< 0.1%
8.0158045821
 
< 0.1%
7.8674516711
 
< 0.1%

tempo
Real number (ℝ)

Distinct28373
Distinct (%)59.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.896414819 × 10-17
Minimum-2.94172616
Maximum3.446892983
Zeros0
Zeros (%)0.0%
Negative26234
Negative (%)55.2%
Memory size371.3 KiB
2021-09-10T00:11:26.740893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2.94172616
5-th percentile-1.490852744
Q1-0.8016148555
median-7.812680192 × 10-15
Q30.6731563094
95-th percentile1.829783319
Maximum3.446892983
Range6.388619143
Interquartile range (IQR)1.474771165

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-1.266410829 × 1016
Kurtosis-0.2943275346
Mean-7.896414819 × 10-17
Median Absolute Deviation (MAD)0.7052662254
Skewness0.3404009686
Sum-3.751665645 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:26.897863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.812680192 × 10-154720
 
9.9%
0.00135639020616
 
< 0.1%
0.688807531216
 
< 0.1%
-0.685751145416
 
< 0.1%
0.345511467315
 
< 0.1%
0.00200924029314
 
< 0.1%
-0.857450718214
 
< 0.1%
-0.68578550614
 
< 0.1%
0.688944973313
 
< 0.1%
0.000840982243513
 
< 0.1%
Other values (28363)42660
89.8%
ValueCountFrequency (%)
-2.941726161
< 0.1%
-2.9375341751
< 0.1%
-2.9273634581
< 0.1%
-2.924236651
< 0.1%
-2.9003560811
< 0.1%
-2.8466505711
< 0.1%
-2.8219453491
< 0.1%
-2.8114997481
< 0.1%
-2.7416104281
< 0.1%
-2.6033780121
< 0.1%
ValueCountFrequency (%)
3.4468929831
< 0.1%
3.4388182581
< 0.1%
3.4144222811
< 0.1%
3.3697192311
< 0.1%
3.3661800961
< 0.1%
3.3528138491
< 0.1%
3.3009638081
< 0.1%
3.2971497891
< 0.1%
3.2779422531
< 0.1%
3.2239618592
< 0.1%

obtained_date
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size371.3 KiB
0.23344849246835683
42518 
-2.6207386731683058
 
3863
3.0876356581050195
 
746
-5.474925838804968
 
383
-8.32911300444163
 
1

Length

Max length19
Median length19
Mean length18.97619499
Min length17

Characters and Unicode

Total characters901578
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.23344849246835683
2nd row0.23344849246835683
3rd row0.23344849246835683
4th row0.23344849246835683
5th row0.23344849246835683

Common Values

ValueCountFrequency (%)
0.2334484924683568342518
89.5%
-2.62073867316830583863
 
8.1%
3.0876356581050195746
 
1.6%
-5.474925838804968383
 
0.8%
-8.329113004441631
 
< 0.1%

Length

2021-09-10T00:11:27.244859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-09-10T00:11:27.353863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.2334484924683568342518
89.5%
2.62073867316830583863
 
8.1%
3.0876356581050195746
 
1.6%
5.474925838804968383
 
0.8%
8.329113004441631
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
3183539
20.4%
4171224
19.0%
8142168
15.8%
698501
10.9%
293146
10.3%
052867
 
5.9%
550131
 
5.6%
.47511
 
5.3%
944031
 
4.9%
78855
 
1.0%
Other values (2)9605
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number849820
94.3%
Other Punctuation47511
 
5.3%
Dash Punctuation4247
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3183539
21.6%
4171224
20.1%
8142168
16.7%
698501
11.6%
293146
11.0%
052867
 
6.2%
550131
 
5.9%
944031
 
5.2%
78855
 
1.0%
15358
 
0.6%
Other Punctuation
ValueCountFrequency (%)
.47511
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4247
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common901578
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3183539
20.4%
4171224
19.0%
8142168
15.8%
698501
10.9%
293146
10.3%
052867
 
5.9%
550131
 
5.6%
.47511
 
5.3%
944031
 
4.9%
78855
 
1.0%
Other values (2)9605
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII901578
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3183539
20.4%
4171224
19.0%
8142168
15.8%
698501
10.9%
293146
10.3%
052867
 
5.9%
550131
 
5.6%
.47511
 
5.3%
944031
 
4.9%
78855
 
1.0%
Other values (2)9605
 
1.1%

valence
Real number (ℝ)

HIGH CORRELATION

Distinct1612
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-1.770711202 × 10-16
Minimum-1.846442493
Maximum2.167710127
Zeros0
Zeros (%)0.0%
Negative24267
Negative (%)51.1%
Memory size371.3 KiB
2021-09-10T00:11:27.528896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-1.846442493
5-th percentile-1.576134635
Q1-0.8064856149
median-0.03359937442
Q30.7757055894
95-th percentile1.702359773
Maximum2.167710127
Range4.014152621
Interquartile range (IQR)1.582191204

Descriptive statistics

Standard deviation1.000010524
Coefficient of variation (CV)-5.647507753 × 1015
Kurtosis-0.9321729154
Mean-1.770711202 × 10-16
Median Absolute Deviation (MAD)0.7890723397
Skewness0.1328855542
Sum-8.412825991 × 10-12
Variance1.000021048
MonotonicityNot monotonic
2021-09-10T00:11:27.743965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.478717104595
 
0.2%
-0.53536845292
 
0.2%
-0.502996253489
 
0.2%
-0.426112281988
 
0.2%
-0.397786608183
 
0.2%
-0.341135260781
 
0.2%
2.04226785881
 
0.2%
-0.349228310379
 
0.2%
-0.442298381279
 
0.2%
0.201099065178
 
0.2%
Other values (1602)46666
98.2%
ValueCountFrequency (%)
-1.8464424932
< 0.1%
-1.7683445641
< 0.1%
-1.7634887351
< 0.1%
-1.7517538131
< 0.1%
-1.7489212451
< 0.1%
-1.7468979831
< 0.1%
-1.746493331
< 0.1%
-1.744874721
< 0.1%
-1.7444700681
< 0.1%
-1.7416375011
< 0.1%
ValueCountFrequency (%)
2.1677101271
 
< 0.1%
2.1596170781
 
< 0.1%
2.1555705531
 
< 0.1%
2.1474775031
 
< 0.1%
2.1434309781
 
< 0.1%
2.1393844533
< 0.1%
2.1353379291
 
< 0.1%
2.1312914041
 
< 0.1%
2.1272448792
< 0.1%
2.1231983541
 
< 0.1%

music_genre
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.505461893
Minimum0
Maximum9
Zeros4728
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size185.7 KiB
2021-09-10T00:11:27.926963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q37
95-th percentile9
Maximum9
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.869667073
Coefficient of variation (CV)0.6369307168
Kurtosis-1.221351566
Mean4.505461893
Median Absolute Deviation (MAD)2
Skewness-0.002520666845
Sum214059
Variance8.23498911
MonotonicityIncreasing
2021-09-10T00:11:28.057993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
44779
10.1%
54777
10.1%
74770
10.0%
94758
10.0%
64755
10.0%
24745
10.0%
84737
10.0%
34734
10.0%
04728
10.0%
14728
10.0%
ValueCountFrequency (%)
04728
10.0%
14728
10.0%
24745
10.0%
34734
10.0%
44779
10.1%
54777
10.1%
64755
10.0%
74770
10.0%
84737
10.0%
94758
10.0%
ValueCountFrequency (%)
94758
10.0%
84737
10.0%
74770
10.0%
64755
10.0%
54777
10.1%
44779
10.1%
34734
10.0%
24745
10.0%
14728
10.0%
04728
10.0%

Interactions

2021-09-10T00:10:13.221448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:13.555447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:13.788447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:14.035445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:14.251464image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:14.557276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:14.796521image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:14.982734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:15.158734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:15.358490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:15.555524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:15.745528image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:15.950087image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:16.124060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:16.302030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:16.495593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:16.705597image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:16.879598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:17.072624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:17.264636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:17.483096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:17.682127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:17.880452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:20.642766image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:20.849765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:21.085769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:21.283804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:21.479765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:21.678799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:21.865800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:22.052770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:22.238768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:22.428771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:22.631796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:22.812771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:22.998800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:23.191803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:23.384728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:23.581756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:23.771759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:23.962758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:24.150759image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:24.330762image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:24.526068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:24.724131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:24.915152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:25.112158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:25.294155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:25.477129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:25.670127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:25.874131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:26.056131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:26.245159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:26.605132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:26.858129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:27.110132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:27.589127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:27.874125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:28.114131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:28.389126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:28.693130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:28.982128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:29.309128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:29.684126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:30.033130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:30.313132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:30.582132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:30.878136image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:31.112128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:31.411130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:31.744126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:32.095132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:32.474130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:32.789130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:33.118128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:33.320132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:33.503129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:33.696161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:33.890817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:34.081819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:34.270783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:34.447781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:34.623781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:34.846786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:35.087781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:35.261816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:35.451788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:35.648814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:35.840751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:36.031750image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:36.220754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:36.413752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:36.605788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:36.784753image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:36.988754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:37.177757image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:37.521752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:37.718756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:37.906406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:38.089407image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:38.283415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:38.505376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:38.721372image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:38.902377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:39.093373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:39.283380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:39.472912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:39.661915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:39.841911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:40.051879image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:40.258876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:40.449912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:40.634880image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:40.820023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:41.002024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:41.172050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:41.343017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:41.672021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:41.870020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:42.050056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:42.219018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:42.400013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:42.582982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:42.756010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:42.935023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:43.187982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-10T00:10:43.592985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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Correlations

2021-09-10T00:11:28.579959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-10T00:11:28.949994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-10T00:11:29.317994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-10T00:11:29.684995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-10T00:11:29.995994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-10T00:11:20.332481image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-10T00:11:20.837548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexartist_nametrack_hashtrack_namepopularityacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempoobtained_datevalencemusic_genre
00-1.489739-1.2584200.5702480.307928-0.896807-0.1477811.532307e-011.336036-0.5350721.632191-0.4635600.562044-0.747331-0.5352577.578378e-010.2334480.1161220
11-1.475657-1.274109-0.3314320.3722720.363890-0.3548841.549090e-02-0.253857-0.556511-0.681049-0.5440080.5401021.338094-0.633170-1.383029e+000.233448-0.1307160
221.081735-1.272096-0.8547320.243584-0.890777-0.472428-1.069141e+001.491240-0.1714451.6321910.7431601.176918-0.7473310.0937591.768622e+000.2334481.5809640
33-0.308863-1.5424291.2166150.243584-0.577463-1.2000853.227442e-010.253394-0.5585761.053881-0.5625730.555868-0.747331-0.544158-1.466148e+000.233448-1.0047650
441.364381-1.267649-0.2948960.372272-0.888167-1.3288243.698013e-020.832570-0.558576-0.6810490.9040560.7583881.338094-0.507565-7.812680e-150.233448-1.0452310
55-0.882201-1.175104-0.176461-0.142480-0.046669-0.774686-2.761231e-160.847712-0.558576-1.259359-0.4759370.8811021.3380940.726732-1.382583e+000.2334480.0068660
660.584338-1.419512-1.510189-0.206824-0.895748-1.614289-1.039380e-010.628155-0.558556-1.548514-0.6591110.637623-0.747331-0.5352571.648085e+000.233448-1.0816490
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Last rows

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